Path Tracing  System Employing Distributed Accelerating Structures

ABSTRACT

A path tracing system in which the traversal task is distributed between one global acceleration structure, which is central in the system, and multiple local acceleration structures, distributed among cells, of high locality and of autonomous processing. Accordingly, the centrality of the critical resource of accelerating structure is reduced, lessening bottlenecks, while improving parallelism.

CROSS-REFERENCE TO RELATED CASES

The present application is a continuation of the U.S. application Ser.No. 15/984,359 filed May 20, 2018 entitled “Path Tracing MethodEmploying Distributed Accelerating Structures”; which is a continuationof U.S. application Ser. No. 15/376,580 filed Dec. 12, 2016 entitled“Path Tracing Method Implemented on Cells and Employing DistributedAcceleration Structures”; which claims the benefit of U.S. ProvisionalApplication Ser. No. 62/266,584, filed on Dec. 12, 2015, of U.S.Provisional Application Ser. No. 62/289,927, filed on Feb. 2, 2016, ofU.S. Provisional Application Ser. No. 62/354,755, filed on Jun. 26,2016, and of U.S. Provisional Application Ser. No. 62/408,730, filed onOct. 15, 2016, and is a continuation-in-part of the U.S. applicationSer. No. 15/009,442 filed Jan. 28, 2016 entitled “Shadowing Method forRay Tracing Based on Geometrical Stencils” (U.S. Pat. No. 9,741,160);which is a continuation-in-part of the U.S. application Ser. No.14/868,461 filed Sep. 29, 2015 entitled “Method and Apparatus for anInter-Cell Shortest Communication” (U.S. Pat. No. 9,558,530); all ofwhich are hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to new and improved ways for carrying outthe path tracing method of parallel graphics rendering.

BACKGROUND OF THE INVENTION

Path tracing is a computer graphic method for a realistic rendering ofthree-dimensional scenes, based on global illumination. Globalillumination takes into account not only the light which comes directlyfrom a light source, but also subsequent cases in which light rays fromthe same source are reflected by other surfaces in the scene, whetherreflective or not (indirect illumination).

Fundamentally, global illumination integrates over all the luminancearriving to a single point on the surface of a rendered object. Thisluminance is then reduced by a surface reflectance function (BRDF) todetermine how much of it will go towards the viewpoint camera. Thisintegration procedure is repeated for every pixel in the output image.When combined with physically accurate models of surfaces, accuratemodels of real light sources, and optically-correct cameras, pathtracing can produce still images that are indistinguishable fromphotographs.

Path tracing naturally simulates many effects that have to bespecifically added to other methods (conventional ray tracing orscanline rendering), such as soft shadows, depth of field, motion blur,caustics, ambient occlusion, and indirect lighting.

Path tracing is a computationally intensive algorithm. The basic andmost time consuming task in path tracing is the locating of intersectionpoints between millions of rays and millions of polygons. In prior artit is done by massive traversals of accelerating structures and byresolving intersection tests. Traversals are typically taking 60%-70% ofrendering time. In addition, the need to modify or reconstructacceleration structures before each dynamic frame, limits theperformance.

Fortunately, path tracing is quite easy to parallelize. The contributionof each ray to the final image can be computed independently of otherrays. There are two main parallelization approaches in the prior art:(i) ray-parallel, in which rays are distributed among parallelprocessors, while each processor traces a ray all the way, and (ii)data-parallel, in which the scene is distributed among multipleprocessors, while a ray is handled by multiple processors in a row.

The ray-parallel implementation, subdividing the image space into anumber of disjoint regions, replicates all the scene data with eachprocessor. Each processor, renders a number of screen regions using theunaltered sequential version of the path tracing algorithm, until thewhole image is completed. Load balancing is achieved dynamically bysending new tasks to processors that have just become idle. However, ifa large model needs to be rendered, the local memory of each processoris not large enough to hold the entire scene. This is evident from FIG.1 where the performance of CPU based rendering systems is compared withthat of GPUs. GPU has a limited amount of video memory, therefore theeffect of performance diminution occurs earlier than in CPU, which hasan unlimited memory. Due to the limitation of local memory, for largemodels a central storage must be used, as pictured in FIG. 2, for thegeometric data, acceleration structures and textures. Each processorneeds a massive access to these resources. Such a centralization ofresources causes a severe bottleneck. The hurdle grows with the datasize, and get even worse when a central mass storage has to be used fora large data. The relatively long access times of a mass storage, levelsof magnitude slower than RAM, become a stoppage for big rendering data.

Data-parallel is a different approach to rendering, best for large datacases that do not fit into a single processor's memory. Each processorowns a subset of the database, tracing rays only when they pass throughits own subspace (cell). As shown in FIG. 3, the subsets of the geometrydata and textures are kept in private memories, each designated aprocessor. The acceleration structures are broken down to small localsubstructures, and distributed among subsets. High locality is achievedby treating the relevant segment of a transitory ray by the local dataand local acceleration structure, with a little need of centralresources. Data locality is a desirable feature in path tracing: itreduces moves of massive data, contributes to a higher utilization ofcache memories, reduces the use of main memory, and decreases the needof massive data moves. The high locality of the data parallel approachmight be advantageous for very large models. However, the efficiency indata parallel rendering systems tends to be low, bringing up severalchallenges. There is a high interprocessor communication due to themassive amount of rays that must pass among the subsets of data. Thesepassages involve a massive interfacing among the local accelerationstructures. Such interfacing must be handled efficiently and wellsynchronized. Furthermore, the amount of communicating rays must bereduced to achieve a satisfactory efficiency.

OBJECTS AND SUMMARY OF THE PRESENT INVENTION

Accordingly, a primary object of the present invention is to provide anew and improved method of and apparatus for path tracing, whilereducing the high complexity associated with the prior art.

Another object of the present invention is to provide a new and improvedmethod of and apparatus for path tracing, while enabling an efficientrendering of big data.

Another object of the present invention is to provide a new and improvedmechanism for locating intersection points between rays and objects forglobal illumination rendering.

Another object of the present invention is to provide a new and improvedacceleration structure mechanism for data parallel path tracing,consisting of global and local components.

Another object of the present invention is to decrease the complexity ofpath tracing by reducing the traversals of acceleration structures.

Another object of the present invention is to provide a new and improvedlocal acceleration structure.

Yet another object of the present invention is to replace the complextraversals of acceleration structures by a new and low complexitymechanism.

Yet another object of the present invention is to replace the complextraversals of acceleration structures by a new and low complexitymechanism implementable by the graphics pipeline.

These and other objects of the present invention will become apparenthereinafter and in the claims to invention.

The embodiments of the present invention follow the data parallelapproach, therefore the scene data are fragmented into numerousnon-uniform sub-volumes of cells. Cell is a basic unit of process anddata locality.

According to one embodiment, the task of traversals is divided betweenthe global acceleration structure, and multiple small local accelerationstructures. The local acceleration structures, along with the localgeometry data and textures reside in cells. Each cell is assigned aprocessor, on a demand driven basis. These rendering processors may comeon different platforms of CPUs, GPUs or both. Each cell builds its ownacceleration structure for the local portion of data. It means that theglobal acceleration structure remains the only central element, whileits size and load are greatly reduced. Each cell handles ray traversalfor its local domain only, meaning that there is no need to retrievedata from external devices (central memory or hard disks), saving thebig penalty of slow access times. The secondary (the term ‘secondary’generally stands for secondary, ternary, and higher generations of HIPsand bouncing rays) rays are generated locally at each cell.

Another embodiment of the present invention replaces the localacceleration structures with a new and improved method and apparatus forlocating ray/object intersections. It comprises a low complexitycollective shooting method in a cell, facilitated by the graphicspipeline. According to this method, the encounter between the ray andobject is projected by ‘visualizing’, in a sense similar to the humanseeing, eliminating the need for expensive line/object mathematicalintersections. The communication of rays among cells is still carried bythe global acceleration structure. However, this communication isreduced: due to many cell-internal rays that do not use the globalacceleration structure, and due to lowering the traversal complexity byknowing-ahead the intersection coordinates. This reduces greatly theamount of traversals of secondary rays, and off loads the globalacceleration structure which otherwise, due to its centrality, would besubject to bottleneck effect.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of how to practice the Objects of thePresent Invention, the following Detailed Description of theIllustrative Embodiments can be read in conjunction with theaccompanying Drawings, briefly described below:

FIG. 1. Prior Art. Performance degradation of CPU and GPU basedrendering, as a function of data size

FIG. 2. Prior art. Ray parallel approach. When the local memory of eachprocessor is not large enough to hold the entire scene, a central datarepository has to be used

FIG. 3. Prior art. Data parallel approach.

FIG. 4a . A cell with local acceleration structure, local data and localtexture

FIG. 4b . An example of a global acceleration structure

FIG. 4c . An example of a local acceleration structure

FIG. 5a . Data parallel system according to an embodiment of presentinvention

FIG. 5b . Generation of primary rays according to an embodiment ofpresent invention

FIG. 5c . A preferable platform according to an embodiment of presentinvention

FIG. 5d . Flowchart of generating a primary HIP

FIG. 5e . Flowchart of generating a secondary HIP

FIG. 6. Diffuse radiance calculation at a point of a primary hit

FIG. 7. The principle of local collective shooting

FIG. 8a . Parallel projection of a sub-scene

FIG. 8b . A separate HIP data of the sub-scene

FIG. 8c . A separate geometry data of the sub-scene

FIG. 9a . A HIP rendered for a depth mask

FIG. 9b . Rendering the geometric data using the previously createddepth mask

FIG. 10. Different cases of projection rays in a sub-scene

FIG. 11. Lack of accuracy between projection rays and HIPs

FIG. 12. Intersection test compensating for lack of accuracy

FIG. 13. Multiple projections on a single HIP

FIG. 14. New HIPs generated by successive projections

FIG. 15. Different cases of secondary rays

FIG. 16a . Hemi-ray hitting out of cell

FIG. 16b . Traversal of global acceleration structure by a hemi-ray

FIG. 17. Flowchart of a single projection-cycle in a cell

FIG. 18. Flowchart of a collective shooting in a cell

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

According to an embodiment of the present invention, the task oftraversals is divided between the global acceleration structure, andmultiple small local acceleration structures, as depicted in FIG. 4b andFIG. 4b , respectively. As shown in FIG. 4a , the local accelerationstructure 41 resides in the cell, along with the local geometry data 42and textures 43. Each cell is assigned a processor, on a demand drivenbasis. These rendering processors may come either as a multiple CPU or amultiple GPU based platforms.

The basic data elements of the global acceleration structure, FIG. 4b ,are cells. They are leaves of the binary tree 44, replacing thetriangles (or other 3D primitives) of the prior art. The leaves of thelocal acceleration structure, FIG. 4c , are triangles 45. This partitionof the acceleration structure into two components, shrinks the globalpart of it by many levels, improving the performance. E.g. for ageometric data of 8 million triangles which are distributed among 8,000cells, the global binary tree of acceleration structure, instead ofhaving 23 levels and ^(˜)8,000,000 leaves as a full tree of prior art,in will shrink to only 10 hierarchical levels and ^(˜)8,000 leaves,dramatically reducing the central traversal load. An average local treewill keep 1,000 triangles. Each cell builds its own accelerationstructure for an autonomous rendering of its local sub-space data. Itmeans that the only central element remains the global accelerationstructure, however, as described hereinafter, its size and load aregreatly reduced.

As shown in FIG. 5a , the cells are first populated by primary hitpoints (HIPs), following the shootings of primary rays. The primary raysare preferable projected by GPU. The intersection points ray/objects arefound by their coordinates in the scene space. These points must bedistributed to their corresponding cells. Each projected intersectionpoint is first moved to the global acceleration structure, and thennavigated by its [x,y,z] coordinates into its hosting cell. Once thehost cell is reached, the HIP is put in place by traversing the localtree. A primary HIP is a source for secondary rays. A secondary ray,shot from primary HIP could transfer through multiple cells to find thetarget cell. As shown in FIG. 5a , also the inter-cell communication isdone by traversing the global acceleration structure.

Each cell of transit treats its corresponding segment of ray vs. localdata, meaning that there is no need to retrieve data from externaldevices (central memory or hard disks). This saves the penalty of slowaccess times. The secondary (and higher) rays are generated locally ateach cell. Some of them terminate at the cell, hitting local objects,the rest is communicated to other cells utilizing the globalacceleration structure.

When a cell receives rays, the rays are rearranged into coherentpackets, in order to gain an increased performance, utilizing thearchitectural advantage of the platform. Today's CPU architectureprovides extensive SIMD abilities (up to 512 bits:16 floats in currentarchitecture), which can be utilized to perform parallel traverse onacceleration trees. This method, known as packet traverse, would providesuperior traverse and intersection test performance, as long as the raysin the packet are correlated. This applies even more strongly to GPUplatforms, in which memory coherency is crucial. A natural coherence ispresent only on the primary rays, as long as their path is short enough.Secondary rays must be brought to coherency in order to apply the packetmethod, which improves the advantage drastically.

According to an embodiment of the current invention, the rays that movebetween local and global structures, are rearranged into packets on thetransition phase, so the packets in the entry to the local accelerationstructure would comprise coherent rays.

Reordering is preformed in a few levels. First, a packet with rays alltargeted to the same cell. Further sorting can be done, so all thepacket rays enter the cell at the same face. A packet can be rearrangedseveral times on its course, to keep the coherency. There is a cost, inlatency and in processing, but the advantage of using packetsoutperforms the cost.

As mentioned, a GPU hardware is employed for the primary rays. In orderto randomize the primary rays, such that the HIP samples (as describedhereinafter in FIG. 6) will be stochastically dispersed in the scene531, a fluctuation of projected rays is done. The fluctuating rays 532,related to a certain pixel 530, are kept intact with the pixel, whilethe camera 533 moves accordingly. The cameras for all the screen pixelsis moved in a unified way.

As mentioned above, and as it is shown in FIG. 5a , the rendering of theembodiment can be preferably split between GPU and CPU. Nevertheless,other implementations are possible as well, e.g. GPU only or CPU only.The GPU creates primary rays and operates the global accelerationstructure. The CPU must perform with multiple cores and threads, for aparallel computing of cells. The multicore CPU 510, shown in FIG. 5c ,is an example of a preferable platform. It has an integrated GPU 516 andfour CPU cores 512-515. All of them reside on a single piece of silicon,having fast interprocessor communication and shared memory 517. Forbigger rendering data a discrete GPU 518 can be added, and the multicorecan be replaced by a high end CPU with many cores.

The way the distributed acceleration structures, global (GAS) and local(LAS), work for path tracing is drafted in the flow charts of FIGS. 5dand 5e . The first flow chart, showing how the primary HIP aregenerated, starts with mapping of the 3D scene space into cells 541.This knowledge is not new, and is described in the applicationsincorporated herein by reference. Next, accelerating structures areconstructed, a single global structure 547, and multiple localstructures 548, one for each cell. Then, the scene is rendered by atraditional 3D graphics pipeline (raster hardware) for rays thatintersect an object 542. These rays provide the coordinates of theintersection points, to be pinpointed by primary HIPs (hit point). Thesecoordinates must be matched with their target cell, which will be foundby traversing the GAS 544-545. Once the target cell is found, theprimary HIP is created 546. The second flow chart, in FIG. 5e , showshow the secondary HIP is generated. Referring to FIG. 6, the secondaryray is taken randomly from the hemisphere 601 of the primary HIP 606.Such a secondary ray, called hemi-ray, can either hit an object in thecell, or leave the cell and continue seeking in another cell. Thesecondary ray can start anew from a local HIP, or can continue anincoming ray 551. First the LAS is traversed for a local hit 552. If anintersection point is found, this point's emittance is sampled for theHIP of origin, and a child HIP is created 553. Otherwise, the ray leavesthe cell, and will traverse the GAS, for the next cell 554.

The computed global illumination simulates the real world, where objectsand surfaces are visible due to the fact that they are reflectingdiffused light. This reflected light illuminates other objects in turn,by diffused inter-reflection. Diffuse inter-reflection is a processwhereby light reflected from an object strikes other objects in thesurrounding area, illuminating them. Diffuse inter-reflectionspecifically describes light reflected from objects which are not shinyor specular. It is an important component of global illumination.Generally, two types of rays are used in path tracing. The primary raysare shot from the viewer's eye via screen pixels into the scene. Theprimary ray hits an object in the primary hit point (HIP). Secondaryrays are then generated, bouncing further into the scene, generatingnext generations of HIPs.

The calculation of a diffuse radiance at a point of a primary hit on anobject is depicted in FIG. 6. The reflected radiance on a surface point,HIP 606, is computed by integrating the incoming radiance over ahemisphere 601, centered at that point on the surface and oriented suchthat its north pole is aligned with the surface normal. The BRDF(bidirectional reflectance distribution function) is a function thatdefines how light is reflected at an opaque surface. This lightingequation is too expensive for more than just a small number ofdirections. Therefore, a smaller number of rays are generated randomlyto compute the illumination of a surface.

HIP 606 absorbs reflected radiance from its surroundings. Upon a hit ofthat ray at some external hit point 605, the amount of reflected lightfrom that hit point is reported to the pixel of origin. The actual rayshooting provides reflectance results, and generates additional rays inthe scene space. Each of the hemi-rays 604, is of a differentprobability, according to a probability distribution function.

Collective shooting of secondary rays. According to another embodimentof the present invention, the heavy traversals of local accelerationstructures are replaced by new and improved method of collectiveshooting in a cell, greatly reducing the processing complexity.

According to the embodiment, the encounter between a ray and object isprojected by a visualization mechanism, in a sense similar to the humanseeing, eliminating the need for a line/object mathematicalintersection. This method replaces the expensive traversals ofacceleration structure. As explained hereinafter, the visualization isdone by means of the graphics GPU pipeline, resulting in highperformance and low complexity.

In the following description it is assumed, that the scene is subdividedinto cells, while each cell is processed autonomously. However, thecollective shooting mechanism can be used as well as a stand aloneversion, when no cells involved.

The collective shooting is mathematically articulated as follows:

Let T be a tree-graph of d levels and let V be its vertices on top ofgeometries G in space.

Define V_(d)—vertices within V in level d.

Let C_(d) be a division of V_(d) to clusters.

We shall extend T to d+1 levels by finding V_(d+1):

Choose cluster C∈C_(d), with V_(d) _(c) vertices and define L_(c)—set ofmappings from V_(d) _(c) to V_(d+1) _(c) such that V_(d+1) _(c) areprojections of the vertices in V_(d) _(c) on top of G.

$V_{d + 1}:={\bigcup\limits_{c}V_{d + 1_{c}}}$

-   -   Note that L_(c) is a set of mappings from the same input, so        there can be several target vertices for any input vertex.

Instead of projecting every vertex v∈V_(d) _(c) on every possiblegeometry g∈G in θ(|L_(c) |·|V|·|G|), we project every possible g∈G onevery cluster c∈C_(d) in θ(|L_(c) |·(|V|+|G|)).

-   -   In R³ We can utilize traditional 3D graphics pipeline (raster        hardware) to achieve fast mappings (projections) in parallel.

We optimize C_(d)/L_(c) in throughput/overfitting to have:

-   -   Maximum number of vertices per cluster in average (throughput).    -   Minimum number of [discrete] projections of geometries fitting        all vertices (overfitting).    -   Preprocess/Runtime constraints.

L_(c) is chosen to have a pseudo-random output, representing a possiblesegment of distribution for each v∈V_(d) _(c) to simulate a physicalscenario.

The input vertices V of the above articulation are illustrated in FIG. 7as the primary HIPs, e.g. 708, each generating multiple target verticesV_(d+1), e.g. 709. Several HIPs are shown in the cell 700. Following theglobal illumination principle, each HIP must shoot multiple hemi-rays inrandom directions within the hemisphere, in order to sufficiently samplethe surroundings. In prior art each HIP is treated individually, whileaccording to the embodiment, the hemi-rays from all HIPs are generatedand shot collectively, utilizing a graphics pipeline projectionmechanism. The projection can be parallel, oblique, perspective orother. In the example given in FIG. 7 and hereinafter, a parallelprojection is assumed 701. The view, taken from the parallel projection,is registered on a rendering target 702 as a texture. E.g. the radiancegained by the projected hemi-ray 706, shot as part of the parallelprojection at angle α, and hitting an object at a hit point 709, isregistered at fragment 705 of the render target. The same procedure iscarried on to the next generations of HIPs: e.g. the new HIP 709 becomesa subject for the next generation of local collective shooting. Asatisfactory number of repeating projections, each projection at arandom direction, would have an equivalent effect to the prior art'sindividual treatments of all the HIPs in the cell.

As explained hereinafter, the projecting mechanism must treat the HIPsseparately from the geometry data. This is depicted in FIGS. 8a, 8b, and8c . FIG. 8a , showing a parallel projection of a sub-scene, consistingof a projection plane 80, projection rays 81, HIPs 82 and geometric data(triangle) 83. The cell sub-space contains HIPs and geometric data oftriangles, which are projected separately. In FIG. 8b only the HIPs areshown. Each single parallel projection is relevant only to those HIPsthat are facing toward the projection, the ‘active’ HIPs, like 82, butnot 84. For all relevant HIPs, or part thereof, a hemi-ray is generatedin the projected direction. In FIG. 8c the geometric data of thesub-scene is shown. It is subject to a separate rendering, as describedbelow.

The local collective shooting of the present invention utilizes theZ-buffering mechanism of a raster graphics. Each active HIP ‘looks’forward along the direction of parallel projection. So the Z-bufferingmechanism must discard objects all the way before the HIP, and startseeking objects only at the HIP. This is described in FIG. 9b . Triangle95 that is located earlier than the HIP 91, is discarded. One embodimentof doing it is based on a selective use of the z-buffering mechanism ofGPU, e.g. the function gIDepthMask of the OpenGL graphics library. Theparallel projection is done in two separate rendering passes. In thefirst pass the HIPs only are rendered, to generate a HIP depth mask, asshown in FIG. 9a . The ray 92, that passes through HIP 91, is brokendown into 2 segments. The first segment, termed early segment, extendsfrom the projection plane 90 up to the depth point Z_(HIP) at the HIP.This depth value Z_(HIP) is registered in the HIP depth mask, to be usedin the second pass, when the geometry data is rendered, in order tofilter out the earlier segment. Only the hits of the main segment areconsidered, as shown in FIG. 9b . The geometric data is rendered intorender target, utilizing the HIP Depth Mask. The depth value Z_(HIP) isused to discard the segment between the projection plane 90 and thelocation of HIP 91. The triangle 95 turns invisible, because thefunctional rendering starts right after the HIP along the ray, whichhits the triangle at fragment 93. Furthermore, rays that miss HIPs areentirely discarded, considered as early segments in their entirety. Oncethe projection is done, the hemi-ray/triangle hit can be found byinspecting the render target at the u, v coordinate.

In FIG. 10 three different cases of projection rays are shown. They allpass through the sub-scene of the cell 1006, commencing at theprojection plane 1010 and extending, through the cell, into entire scenespace. Ray 1000 consists of two segments. The first segment, extendingfrom the projection plane up to HIP 1003, is discarded from hittingobjects. Objects encountered on the way of this segment becomeinvisible, like triangle 1009. The ‘functional’ segment starts at theHIP 1003, and thereafter encountering the triangle 1007. This triangle,as being internal to the cell 1006, is recorded as a new HIP belongingto the cell. The ‘functional’ segment of ray 1001, starts at HIP 1004and hits an external triangle 1008. The recording of this triangle isdone in another cell. The third ray 1002 does not hit any object. Ray1012 fails to pass through HIP. It remains in a ‘non-functional’ state,therefore objects, like triangle 1011, remain invisible. Only the‘interesting’ fragments, extended from HIPs, are registered on therender target.

Accuracy: to what extent the hemi-ray and the parallel projection raymust overlap? The lack of accuracy is demonstrated in FIG. 11. Theprojected rays 110 and the hemi-rays mostly do not overlap 111-113.Hemi-ray 114 is the only one to accurately overlap a projected ray.Accuracy has two different aspects. The first is the gained radiancethat is brought back to the HIP from the surrounding scene. If we allowsome tolerance, then the closest projection ray would hit a targettriangle at a close but not accurate location. The resulted radiancewould be most likely close, or almost the same. The second aspect is thenext generation ray, that extends the hemi-ray at a point of hit,bouncing farther into the scene. Here the accuracy is critical. A smallinitial slip can carry an intolerable error with few bouncinggenerations. In FIG. 11 only ray 114, as is, qualifies for the secondaspect. However, the inaccurate cases can be corrected by conducting anintersection test, as explained hereinafter in detail.

In FIG. 12, the projection ray 123 passes close to HIP 121, hitting thetriangle 125 at fragment 122. However, since there is no overlappingbetween the projection ray 123 and the exact hemi-ray 124, the accuratepoint of hit 126 must be calculated. This can be done by performing anintersection test between the exact hemi-ray and the triangle. Asmentioned above, accuracy is mandatory if many generations of HIPs areprocessed. E.g. at the point 126 a continuing hemi-ray must begenerated. Alternatively, if the hit point is required for the sampledradiance only, then a lower accuracy maybe still tolerable. In such acase, the radiance value is taken in fragment 122, avoiding intersectiontest.

Multiple parallel projections at a cell, and their effect on a HIP 134,are shown in FIG. 13. The projection planes 131, 132, and 133, can bechosen randomly. Three different rays pass through the HIP, while eachray's ‘functional’ segment begins at the HIP. It is equivalent to threehemi-rays emitting from the HIP, within the hemisphere boundaries. Therays are marked p1, p2 and p3, identifying that they belong toprojections 131, 132, 133 respectively. In the case shown, the rays hitthree different objects, 137, 136 and 135, respectively, generatingthree newly created HIPs, and collecting 3 radiance values. Theresulting radiance is passed to the pixel 138.

The next generations of rays and HIPs may be generated and used indifferent ways. According to one embodiment, use is made of all HIP'shemi-rays. This is shown in FIG. 14, where 3 successive projections aregenerated, creating new HIPs inside and outside the cell, up to thedepth of 3. First, a single HIP of origin 1411 “shoots” a hemi-ray in P1direction, creating a new HIP 1422. Next projection P2 creates twoadditional HIPs 1423 and 1424. Then projection P3 generates 4 additionalHIPs 1425-1428. Some of the newly generated HIPs 1424 are internal tothe cell, and some external, such as 1428. The reproduction rate of HIPsaccording to this embodiment is exponential, thus hard to control. E.g.another embodiment can use only one of HIP children to continue thegeneration.

In the collective shooting within a cell, the communication of raysreaching out of the cell, is still carried by the global accelerationstructure. However, this communication is reduced due to two procedures:many rays conclude their tracing internally in the cell, and thetraversal is simplified by knowing ahead the coordinate of the externalintersection, found in the course of the projection. These events reducethe load on the global acceleration structure, as shown in the next twodrawings.

FIG. 15 pictures an exemplary cell 1515 with primary HIPs, e.g. 1500. Weassume that all the HIPs in the scene are primary. Their response to theprojection 1520 varies according to their relative position. The normalvector of HIP 1500 is heading the opposite direction, therefore it isinactive for this projection. The hemi-ray extended from HIP 1501intersects a local object 1507, therefore it does not extend outside thecell, and does not need external services. The hemi-ray of HIP 1502,reaches outside the cell, ad must use the global acceleration structureto create a new HIP at the intersection point with the object 1510. Inthis example, half of the hemi-rays reach out of the cell.

The reduced traversal complexity involved with the use of the globalacceleration structure is described by FIG. 16a and FIG. 16b . As shownin FIG. 16a the primary HIP 1601 shoots a hemi-ray 1603 which hits anexternal object at coordinates [x,y,z]. This coordinate is easilycalculated from the location [u,v] of the corresponding fragment. Thecell is not known. Next step is to traverse the global accelerationstructure for the cell holding this coordinate. Whereas, if thecoordinates were not known, the traversal should include a visit in theintermediate cells of C and D.

Flowchart. The preferred embodiment of the present invention isflowcharted in FIGS. 17 and 18. FIG. 17 describes a singleprojection-cycle, out of multiple projections in a cell. It is assumedthat the cell is populated above some minimal number of HIPs. Theparallel projection consists of two passes. The first pass 1701generates a HIP depth mask. The HIP depths of the mask are utilized inthe second pass to disable the insignificant early segments of rays.Rays that do not pass HIPs are entirely discarded. The second pass 1702renders the geometric data, in reference to HIP depth mask, into therender target. Only triangles that are hit by the ‘functional’ segmentsof parallel rays, are candidates for a subsequent hit test.

The product of rendering, the render target, is used to find the hitpoint for each HIP, by inspecting the render target texture at thecorrect u,v coordinates 1706. If a hit is found, then the accuracy ischecked 1707, as explained hereinbefore. The projection cycle iscompleted when all HIPs are checked for hit and for accuracy.

The preferred embodiment of a collective shooting in a cell is detailedin flow chart of FIG. 18. First, multiple parallel projections in a cellare done, each at random direction. This sequence of projectionsculminates with a list of multiple hemi-ray hits per each HIP 1801.Although, in this embodiment, only one hemi-ray per HIP is chosen tocontinue, nevertheless, all the hit points associated with a HIP aretaken advantage of to calculate the accumulated emittance, for indirectillumination. From the multiple hits only the most accurate hemi-ray ischosen to continue the bouncing chain for a HIP 1804. Each hit isregistered in the cell as a local one 1806, or in another cell if itfalls out of the cell 1807. The bouncing sequence is completed when allHIPs are done 1808. When the cell processing is done, the cell turnsinactive, and the processor/thread is assigned to another cell in aline. Each cell can turn active multiple times, according to the needs.

What is claimed is:
 1. A system for path tracing big data, employingdistributed acceleration structures, comprising: At least onegeneral-purpose processors, at least one graphics processing unit havingraster pipeline, data of a 3D scene; wherein in run time for generatingprimary hit points a) data structure of cells is generated; b) the dataof 3D scene is mapped onto cells; c) a global acceleration structure ofcells is constructed; d) at each cell a local acceleration structure isconstructed; e) primary rays are generated by rendering the 3D scenewith raster pipeline; f) the global acceleration structure of cells istraversed for each primary ray, to find a cell of intersection; g) a hitpoint is registered at the cell of intersection; and for generatingsecondary hit points a) locally at each cell secondary rays are shotoriginating at each primary hit point, or at each incoming ray; b)locally at each cell the local acceleration structure is traversed byeach secondary ray seeking for hits with local objects; wherein in eventof hit, radiance at the point of intersection is sampled, and diffuseradiance at ray's origin is calculated and registered; wherein in eventof no hit, the global acceleration structure is traversed seeking thenext cell of hit for the ray.
 2. The method of claim 1, whereingenerating primary rays involve fluctuation of projected rays.
 3. Themethod of claim 1, wherein basic data elements of the globalacceleration structure are cells.
 4. The method of claim 1, whereinbasic data elements of the local acceleration structures are triangles.5. The method of claim 1, wherein data elements of a cell's localacceleration structures are the cell's triangles.
 6. The method of claim1, wherein the global acceleration structure resides centrally in thesystem, accessed by primary rays as well as by part of the secondaryrays of local cells.
 7. The method of claim 1, wherein secondary raysthat move between local and global structures, may be rearranged inpackets.
 8. The method of claim 1, wherein local acceleration structureresides in a cell, along with a local sub-space data.